Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-26T01:40:23.541Z Has data issue: false hasContentIssue false

Interaction between bitter taste receptor gene TAS2R38 and obesity-related gene TMEM with respect to energy intake in an adult population

Published online by Cambridge University Press:  24 November 2016

E.L Feeney
Affiliation:
UCD Institute of Food and Health, University College Dublin, Dublin, Republic of Ireland
B.A McNulty
Affiliation:
UCD Institute of Food and Health, University College Dublin, Dublin, Republic of Ireland
J. Walton
Affiliation:
School of Food & Nutritional Sciences, University College Cork, Republic of Ireland
A. Flynn
Affiliation:
School of Food & Nutritional Sciences, University College Cork, Republic of Ireland
E.R Gibney
Affiliation:
UCD Institute of Food and Health, University College Dublin, Dublin, Republic of Ireland
Rights & Permissions [Opens in a new window]

Abstract

Type
Abstract
Copyright
Copyright © The Authors 2016 

Obesity is a multifactorial condition that arises from positive energy balance. Although low levels of physical activity in conjunction with excess energy intake are known contributors, other heritable factors may contribute to these(Reference Maes, Neale and Eaves1). Variation in bitter taste receptor TAS2R38 has previously been associated with increased BMI, mainly in older females, in a number of independent studies(Reference Feeney, O'Brien and Scannell2, Reference Tepper, Koelliker and Zhao3, Reference Dotson, Shaw and Mitchel4), potentially via increased preference for energy-dense foods. The gene TMEM18 also associates with increased BMI and body weight in other studies(Reference Almen, Jacobsson and Shaik5), and is thought to act via impaired energy homeostasis(Reference Almen, Jacobsson and Shaik5).

The aim of this analysis was to examine interactions between known SNPs within these genes, and reported nutrient intakes. Using data from the National Adult Nutrition Survey (NANS), approved by the University College Cork Clinical Research Ethics Committee of the Cork Teaching Hospitals, mean daily intake intakes were calculated for n 1500. Nutrient data were used to create a Healthy Eating Index (HEI) score, as per previously published methods(Reference McCullough, Feskanich and Stampfer6). Genetic data was available for a subset, and analysis was completed on n = 1083 with both nutrient intake and genetic data. Associations between genotypes in the aforementioned genes (rs10246939 in TAS2R38 and rs6548238 TMEM18) and anthropometric measures (BMI,  % body fat) and dietary intakes (reported mean daily intakes) were examined via ANCOVA using SPSS v20 for Mac (IBM).

BMI, adjusted for gender, energy intake and age) was significantly different across variants of TMEM18 (P < 0·005). Energy intake was significantly different across genotypes of both SNPs, and ANCOVA revealed a significant interaction between the two with respect to energy intake (adjusted for age and BMI) (p < 0·05 for the interactive term).

Mean HEI score, a measure of the overall quality of the diet, was 25·3 (9·5 SD) in the overall cohort. The mean HEI score was lower in the ‘at-risk’ individuals (24·2, 13·9 SD) compared to the lower-risk individuals (30·7, 13·9 SD), but not significantly so (P > 0·01). These data, although limited, support the idea that heritable variation in BMI and energy intake may be linked to differences in food choices. Considering that as much as 70 % of BMI variation may be heritable(Reference Maes, Neale and Eaves1), improving our understanding of the role of genetic variation on food choice, and the identification of higher-risk individuals based on multiple gene-gene interactions, at earlier life stages may be key developments in the combat of obesity.

This work was supported by the Food Institutional Research Measure (FIRM) through the National Development Plan 2006–2011.

References

1.Maes, HH, Neale, MC, Eaves, LJ (1997) Behav Genet 27, 325351.Google Scholar
2.Feeney, E, O'Brien, SA, Scannell, AGM et al. (2011) Proc Nut Soc, 70, 135143.10.1017/S0029665110003976Google Scholar
3.Tepper, BJ, Koelliker, Y, Zhao, L et al. (2008) Obesity 16, 22892295.10.1038/oby.2008.357Google Scholar
4.Dotson, CD, Shaw, HL, Mitchel, BD et al. (2010) Appetite 54, 9399.10.1016/j.appet.2009.09.011Google Scholar
5.Almen, MS, Jacobsson, JA, Shaik, JHA et al. (2010) BMC Med Genet 11, 58.10.1186/1471-2350-11-58Google Scholar
6.McCullough, ML, Feskanich, D, Stampfer, MJ et al. (2002) Am J Clin Nutr, 76, 12611271.10.1093/ajcn/76.6.1261Google Scholar